Evaluating the safety of immune checkpoint inhibitors prior to liver transplant.
Bibliographic record
Abstract
614 Background: Liver transplantation (LT) offers a 5-year survival exceeding 70% for selected patients with hepatocellular carcinoma (HCC). Immune checkpoint inhibitors (ICIs) may be used to downstage or bridge to transplantation. This study aims to evaluate the safety of ICIs prior to LT. Methods: Multicenter retrospective study, involving 9 centers, included adults who received ICIs and, subsequent LT, between 2019 and 2023. The ICI cohort was matched by age, sex, liver diseases and transplant date (1:3) with a similar cohort of HCC transplanted patients that did not receive ICIs. Results: The ICI cohort included 45 patients (Table part 1). The most common underlying liver diseases were viral. 15.5% of patients had macrovascular invasion and, 62.2% multifocal disease. The most commonly used ICI was nivolumab (75.6%), followed by atezolizumab-bevacizumab (17.8%). The median follow up was 32.5 months (17.4 - 47.6). The median ICI duration was 147 days (129.5-276.5), with a median interval of 58 days (22–193) between the last ICI dose and LT. There were 8 (17.8%) cases of graft rejection, 2 steroid-resistant leading to 1 graft failure. There were 4 (8%) HCC recurrences. During follow-up there were 6 deaths, 2 related to HCC recurrence and 4 non liver related. When compared with 135 non-ICI cohort patients, there were no differences within the groups (Table part 2). Importantly, we found no significant differences in crude rejection rates (p=0.5). Conclusions: Our study suggest that ICI treatment prior to LT is safe. ICI rejection primarily occurs in the acute post-transplant period (within 3 months), with a low risk of death due to graft failure. Further clinical trials are needed. Part 1: ICI cohort descriptive analysis. N (%); Median (IQR); Part 2: Comparison between cohorts. N (%); Mean (SD). (part 1) ICI-cohort n=45 General data Male 38 (84.4) Age 61.2 (7.6) Etiology of liver diseases Hepatitis C 17 (37.8)Hepatitis B 6 (13.3)MASLD 6 (13.3)Viral +Alcohol 5 (11.1)Alcohol 3 (6.7)Autoimmune 3 (6.7)Other 3 (6.7)Combined viral 2 (4.4) Largest tumor (mm) 39 (22) Macrovascular invasion 7 (15.5) Multifocal 28 (62.2) Locoregional therapy 36 (80.0) Number of locoregional therapies 2 (2) Immunotherapy Nivolumab 34 (75.6) Atezolizumab/bevacizumab 8 (17.8) Pembrolizumab 1 (2.2) Nivolumab+pembrolizumab 1 (2.2) PD1 inhibitor clinical trial 1 (2.2) Days of ICI treatment 147 (203) Radiological tumor response ICI No 17 (37.8) Transplant Deceased donor 43 (95.6) Washout period (days) 58 (171) Induction therapy (anti-thymoglobulin) 8 (17.8) Outcomes Rejection Biopsy proven 8 (17.8) 8 (100) Time to rejection (days) 43 (102) Graft failure Rejection 2 (4.4)1 (2.2) HCC recurrence 4 (8) Deaths 6 (13.3) Follow up Since transplant (months) 32.5 (30.2) (part 2) ICI-cohort (n=45) Non-ICI cohort (n= 135) P Post transplant rejection ra
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".